UC Santa Cruz

Coastal wetlands are highly productive ecosystems and can store large amounts of carbon (C). However, decomposition processes in coastal wetlands also produce and emit greenhouse gasses (GHG), such as methane (CH4) - a potent greenhouse gas that could offset C storage in the wetland soil. Often a patchwork of vegetation and open water, coastal wetlands exhibit strong biogeochemical heterogeneity, resulting in elevated CH4 flux (FCH4) at certain times and locations. These points of elevated FCH4, termed “hot spots and hot moments" (HSHM), experience biogeochemical rates so high they can disproportionally contribute to annual flux rates. Despite the broad utilization of the term HSHM, there is no standardized, statistically rigorous method for identifying HSHM and quantifying their impact on ecosystem processes. Furthermore, the conditions that trigger HSHM of FCH4 are poorly understood, and hot moments are often excluded from wetland FCH4 upscaling and predictive modeling. This study presents a comparative analysis of standard HM identification techniques to find the best HM detection method for coastal wetlands and formalize HM identification best practices. We found that using a rolling Z-score threshold to identify hot moments from eddy covariance (EC) flux data was most suitable for coastal wetlands. Using this approach, we flagged hot moments at nine wetlands in the San Francisco Bay-San Joaquin River Delta (Bay-Delta). We then used the identified HMs to train several data-driven Random Forest (RF) models that leverage EC data to predict the occurrence of HMs. The best performing RF accurately (79%) captured HM absence/presence in the Bay-Delta region, and the relative importance of predictive environment parameters in the model shed light on the best predictors for HM. The method comparison in this study provides a best practices workflow for researchers when defining HSHM, and the RF HM model provides an upscaling methodology that could be used to predict the occurrence of HM FCH4 at sites without EC towers. Thus, the HM identification methodology and the predictive model present a valuable tool for wetland managers and restoration planners who can use the information to prioritize time and resources for mitigating and preventing these rare but high-impact emission events.